MicroRNAs (miRNAs) are a group of small non-coding RNAs that post-transcriptionally control expression of genes by targeting mRNAs. miRNA alterations partake in the establishment and progression of different types of human cancer. Consequently, expression profiling of miRNA in human cancers has correlations with cancer detection, staging, progression, and response to therapies. Particularly, amplification, deletion, abnormal pattern of epigenetic factors and the transcriptional factors that mediate regulation of primary miRNA frequently change the landscape of miRNA expression in cancer. Indeed, changes in the quantity and quality of miRNAs are associated with the initiation of cancer, its progression and metastasis. Additionally, miRNA profiling has been used to categorize genes that can affect oncogenic pathways in cancer. Here, we discuss several circulating miRNA signatures, their expression profiles in different types of cancer and their impacts on cellular processes.

Maternally expressed gene 3 (MEG3) is a long non-coding RNA (lncRNA) located on chromosome 14q32.3. Direct sequencing experiments have shown monoallelic expression of this lncRNA. Several studies have shown down-regulation of this lncRNA in human cancers. In some cases, hypermethylation of the promoter region has been suggested as the underlying mechanism. Functional studies have shown that this lncRNA controls expression of several tumor suppressor genes and oncogenes among them are p53, RB, MYC and TGF-β. Through regulation of Wnt-β-catenin pathway, it also affects epithelial-mesenchymal transition. In vitro studies have demonstrated contribution of MEG3 in defining response to chemotherapeutic agents such as paclitaxel, cisplatin and oxaliplatin. Certain polymorphisms within MEG3 are implicated in cancer risk (rs7158663, rs4081134 and rs11160608) or therapeutic response of cancer patients (rs10132552). Taken together, this lncRNA is regarded as a putative cancer biomarker and treatment target. In the current review, several aspects of the participation of MEG3 in carcinogenesis are discussed.

Exosomes are naturally occurring nanosized particles that aid intercellular communication by transmitting biological information between cells. Exosomes have therapeutic efficacy that can transfer their contents between cells as natural carriers. In addition, the exosomal contents delivered to the recipient pathological cells significantly inhibit cancer progression. However, exosome-based tumor treatments are inadequately precise or successful, and various challenges should be adequately overcome. Here, we discuss the significant challenges that exosomes face as drug carriers used for therapeutic targets and strategies for overcoming these challenges in order to promote this new incoming drug carrier further and improve future clinical outcomes. We also present techniques for overcoming these challenges.

Nuclear Enriched Abundant Transcript 1 (NEAT1) is a long non-coding RNA (lncRNA) which is a crucial structural constituent of paraspeckles and has an indispensable role in the formation of paraspeckle. Paraspeckles as nuclear sub-structures are placed in the nucleus interchromatin area and participate in the regulation of gene expression through various mechanisms including mRNA retaining, mRNA breakage, A-to-I editing and protein seizing. Several studies have noted participation of NEAT1 in the pathogenesis of human malignancies. Notably, studies have revealed distinct roles for this lncRNA in the pathogenesis of cancer. While most cancerous tissues exhibit up-regulation of NEAT1, expression of this lncRNA has been down-regulated in leukemia and multiple myeloma. Such distinctive pattern of expression implies diverse roles for this lncRNA in solid tumor versus hematological malignancies. Critical roles of NEAT1 in regulation of apoptosis, cell growth and proliferation, invasion and metastasis in several tissues indicate that this lncRNA might be a therapeutic target in various cancers. Moreover, expression level of this lncRNA in tumoral tissues has been associated with survival of patients. Consequently, this lncRNA might be regarded as biomarker for patients' follow-up.

Coronavirus disease, first detected in late 2019 (COVID-19), has spread fast throughout the world, leading to high mortality. This condition can be diagnosed using RT-PCR technique on nasopharyngeal and throat swabs with sensitivity values ranging from 30 to 70%. However, chest CT scans and X-ray images have been reported to have sensitivity values of 98 and 69%, respectively. The application of machine learning methods on CT and X-ray images has facilitated the accurate diagnosis of COVID-19. In this study, we reviewed studies which used machine and deep learning methods on chest X-ray images and CT scans for COVID-19 diagnosis and compared their performance. The accuracy of these methods ranged from 76% to more than 99%, indicating the applicability of machine and deep learning methods in the clinical diagnosis of COVID-19.